Conventional approaches explore the inter-image correlation between image-pairs [1]} or a group of relevant images [2]} by using constraints or heuristic characteristics like manifold ranking [3]} and clustering [4]}.
However, the discrimination of hand-crafted descriptors is too limited to face complex scenes, leading to unsatisfactory performance. Recently deep learning based models have greatly improved the performance of CoSOD task.
By leveraging the Convolutional Neural Networks (CNNs) [5]}, [6]}, [7]}, [8]}, [9]} and Recurrent Neural Networks (RNNs) [10]}, [11]}, they learn both single image representation (intra-image saliency) and group-wise semantic representation (inter-image consistency) in an end-to-end supervised manner to detect co-salient objects in image group.
Despite their promising results, we find there are still two major drawbacks that prevent the CoSOD from progressing to the next high level: First, current inter-image relationship modeling is sub-optimal. Second, current methods lack consideration of inter-image separability.
